Hiring for Multiple Roles for Data Scientists:
Skills Required:
- Must have minimum of 2 years of industry experience in developing data science models.
- Must have a Formal Coursework in the field of Data Sciences
- In-depth knowledge and experience in the field of Machine Learning, Deep Learning and statistical learning
- The person should be excellent at Classification (logistic regression, svm, decision tree, random forest, neural network), Regression (linear regression, decision tree, random forest, neural network), Classical optimization (gradient descent, newton rapshon, etc), Graph theory (network analytics), Heuristic optimization (genetic algorithm, swarm theory)
- Should be strong at Deep leaning (CNN, LSTM, RNN, Bi-LSTM)
- Must have thorough mathematical knowledge of correlation/causation, decision trees, classification and regression models, recommenders, probability and stochastic processes, distributions, priors and posteriors.
- Skilled at scientific programming languages such as Python, R and writing deployable code into production.
- Understand the model lifecycle of cleansing/standardizing raw data, feature creation/selection, writing complex transformation logic to generate independent and dependent variables, model selection, tuning, A/B testing and generating production ready code.
- Knowledge of Numerical optimization, Linear/Non-linear/Integer programming, Statistics, Combinatorial optimization is a plus.
- AI skillsets - hands-on Machine learning and Deep Learning algorithms and platforms, neural networks in any, or all the following areas, specifically, in Data & Analytics use cases
Language - Natural Language Processing, machine translation, emotion detection, language detection, classification
Vision - computer vision, object recognition/tracking, face/gender/age/emotion recognition, OCR/handwriting recognition
Qualifications :
- Bachelors in Computer Science/Statistics/Applied Mathematics/Applied Statistics/Operations Research from a premier institute.
- 2+ years of experience applying data sciences techniques.
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